We investigated the relationship between glucose variability and frailty. Forty-eight type 2 diabetic patients aged ? 65 years were enrolled. The FRAIL scale was used for frailty assessment, and participants were classified into 'healthy &amp; pre-frail' (n = 24) and 'frail' (n = 24) groups. A continuous glucose monitoring (CGM) system was used for a mean of 6.9 days and standardized CGM metrics were analyzed mean glucose, glucose management indicator (GMI), coefficient of variation, and time in range, time above range (TAR), and time below range. The demographics did not differ between groups. However, among the CGM metrics, mean glucose, GMI, and TAR in the postprandial periods were higher in the frail group (all P less then 0.05). After multivariate adjustments, the post-lunch TAR (OR = 1.12, P = 0.019) affected the prevalence of frailty. Higher glucose variability with marked daytime postprandial hyperglycemia is significantly associated with frailty in older patients with diabetes.Selective estrogen receptor modulators (SERMs) were associated with an increased risk of venous thromboembolism (VTE) due to the estrogen effect. In this study, we investigated the effect of SERMs on VTE compared to bisphosphonates (BPs) using the Korean National Health Insurance claims database.
This was a retrospective cohort study. Women over 50 years old who were first prescribed BPs or SERMs for osteoporosis treatment in 2012 were included. The difference in VTE incidence between the SERMs and BP groups was compared. Both groups were followed up for VTE or PE occurrence, death, or until December 2016. The study population was analyzed by 31 matching according to age using a multivariate Cox model.
The hazard ratio (HR) for VTE was 0.72 (95% confidence interval [CI], 0.40-1.28) in the SERMs group compared to BP group. Older age (60-69 vs. 50-59 years HR, 3.77; 95% CI, 2.07-6.86 and 70-79 vs. 50-59 years HR, 5.88; 95% CI, 3.14-11.02), major osteoporotic fracture (HR, 1.77; 95% CI, 1.16- 2.70), atrial fibrillation (HR, 3.31; 95% CI, 1.35-8.11), and estrogen replacement (HR, 3.40; 95% CI, 2.01-5.73) all increased VTE risk. In subgroup analysis of the SERMs group, past hospitalization (HR, 2.24; 95% CI, 1.02-4.92), estrogen replacement (HR, 5.75; 95% CI, 2.29-14.39), and glucocorticoid replacement (HR, 2.71; 95% CI, 1.05-7.0) increased VTE risk.
SERMs did not increase the risk of VTE compared to BPs in Koreans with osteoporosis. However, old age and estrogen replacement both increased VTE risk.
SERMs did not increase the risk of VTE compared to BPs in Koreans with osteoporosis. However, old age and estrogen replacement both increased VTE risk.The frequencies of South Korean soldiers' depression and resulting suicide are increasing every year. https://www.selleckchem.com/products/LY335979.html Thus, this study aimed to develop and confirm the reliability and validity of a simple short form depression screening scale for soldiers.
This study was conducted as part of a 2013 research project named 'The Epidemiological Study on the Prevalence of Depression in Military Service and a Search for High Risk Group Management.' Clinical depression was diagnosed using the Korean version of the Mini International Neuropsychiatric Interview and suicide risk was assessed through the Korean version of the Composite International Diagnostic Interview. Furthermore, the Center for Epidemiological Studies for Depression Scale (CES-D), the Stress Response Inventory, and the Barret Impulsiveness Scale were employed. Of the 20 CES-D items, three of the most correlated items with clinical diagnosis were derived to form the short form scale. Analyses for internal consistency, concurrent validity, and factor analysis we validity. Since it comprises only three items, it can be utilized easily and frequently. It is expected to be employed in a large-scale suicide prevention project targeting military soldiers in the future; it will be beneficial in selecting high-risk groups for depression.
The depression screening questionnaire for Korean soldiers developed through this study demonstrated high reliability and validity. Since it comprises only three items, it can be utilized easily and frequently. It is expected to be employed in a large-scale suicide prevention project targeting military soldiers in the future; it will be beneficial in selecting high-risk groups for depression.Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea. For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification.
We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers.
The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81-0.9), KNN (AUROC, 0.89; 95% CI, 0.85-0.93), RF (AUROC, 0.86; 95% CI, 0.82-0.9) and BERT (AUROC, 0.82; 95% CI, 0.75-0.87) achieved excellent classification performance. Based on SHAP, we found "", "", "", "", "" and "" were the important vocabularies for determining KTAS and symptoms.
We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.
We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.